A large scale group decision making approach in healthcare service based on sub-group weighting model and hesitant fuzzy linguistic information. (June 2020)

• Record Type: Journal Article
• Authors: Li, Shengli; Wei, Cuiping
• Is Part Of: Computers & industrial engineering. Volume 144(2020)
• Publication Date: 2020-06
• Subjects: Healthcare management -- Large scale group decision making -- TOPSIS -- Possibility distributed hesitant fuzzy linguistic term sets -- Sub-group weight; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106444
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We develop a large-scale group decision making approach for healthcare management decision making. We propose a novel clustering method. We use possibility distributed extended HFLTSs to model the sub-group hesitation. A new hesitant entropy of PDEHFLTS is proposed and applied to characterize the level of togetherness of the sub-group members. We propose a new sub-group weighting method. Abstract: Globally growing demand for healthcare has highlighted increasing requirements for healthcare management. Healthcare management is complex, and multi-faceted, with many stakeholders, all of whose opinions require consideration. Multi-criteria group decision making is thus necessary for effective healthcare decision making. The aim of this paper is to develop a large-scale group decision making (LSGDM) approach for healthcare management decision-making. Hesitant fuzzy linguistic term sets (HFLTSs) are used to describe the decision information. A clustering method based on the ideal points is proposed to cluster the decision makers (DMs) into several sub-groups. Then DMs' preferences are fused by possibility distributed extended HFLTSs (PDEHFLTSs) so as to retain as much as decision information as possible. Based on the sub-group size and the proposed hesitant entropy of PDEHFLTSs, a sub-group weighting model is developed to derive the ranking with multiples and interval forms of the sub-group weights. The final weights of sub-groups are then determined by an optimization model which is derived by calculating the shortest distance from the PDEHFLTS positive ideal solution and the farthest distance from the PDEHFLTS negative ideal solution. An example for healthcare management is presented to illustrate the validity of the proposed model.